Search (13 results, page 1 of 1)

  • × theme_ss:"Informetrie"
  • × year_i:[2020 TO 2030}
  1. Zhu, Y.; Quan, L.; Chen, P.-Y.; Kim, M.C.; Che, C.: Predicting coauthorship using bibliographic network embedding (2023) 0.02 
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    Abstract
    Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph-based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real-world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation-based methods were applied to the entities and relationships to generate their low-dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation-based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework.
  2. Zhao, D.; Strotmann, A.: Mapping knowledge domains on Wikipedia : an author bibliographic coupling analysis of traditional Chinese medicine (2022) 0.02 
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    Abstract
    Purpose Wikipedia has the lofty goal of compiling all human knowledge. The purpose of the present study is to map the structure of the Traditional Chinese Medicine (TCM) knowledge domain on Wikipedia, to identify patterns of knowledge representation on Wikipedia and to test the applicability of author bibliographic coupling analysis, an effective method for mapping knowledge domains represented in published scholarly documents, for Wikipedia data. Design/methodology/approach We adapted and followed the well-established procedures and techniques for author bibliographic coupling analysis (ABCA). Instead of bibliographic data from a citation database, we used all articles on TCM downloaded from the English version of Wikipedia as our dataset. An author bibliographic coupling network was calculated and then factor analyzed using SPSS. Factor analysis results were visualized. Factors were labeled upon manual examination of articles that authors who load primarily in each factor have significantly contributed references to. Clear factors were interpreted as topics. Findings Seven TCM topic areas are represented on Wikipedia, among which Acupuncture-related practices, Falun Gong and Herbal Medicine attracted the most significant contributors to TCM. Acupuncture and Qi Gong have the most connections to the TCM knowledge domain and also serve as bridges for other topics to connect to the domain. Herbal medicine is weakly linked to and non-herbal medicine is isolated from the rest of the TCM knowledge domain. It appears that specific topics are represented well on Wikipedia but their conceptual connections are not. ABCA is effective for mapping knowledge domains on Wikipedia but document-based bibliographic coupling analysis is not. Originality/value Given the prominent position of Wikipedia for both information users and for researchers on knowledge organization and information retrieval, it is important to study how well knowledge is represented and structured on Wikipedia. Such studies appear largely missing although studies from different perspectives both about Wikipedia and using Wikipedia as data are abundant. Author bibliographic coupling analysis is effective for mapping knowledge domains represented in published scholarly documents but has never been applied to mapping knowledge domains represented on Wikipedia.
  3. Delgado-Quirós, L.; Aguillo, I.F.; Martín-Martín, A.; López-Cózar, E.D.; Orduña-Malea, E.; Ortega, J.L.: Why are these publications missing? : uncovering the reasons behind the exclusion of documents in free-access scholarly databases (2024) 0.02 
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    Abstract
    This study analyses the coverage of seven free-access bibliographic databases (Crossref, Dimensions-non-subscription version, Google Scholar, Lens, Microsoft Academic, Scilit, and Semantic Scholar) to identify the potential reasons that might cause the exclusion of scholarly documents and how they could influence coverage. To do this, 116 k randomly selected bibliographic records from Crossref were used as a baseline. API endpoints and web scraping were used to query each database. The results show that coverage differences are mainly caused by the way each service builds their databases. While classic bibliographic databases ingest almost the exact same content from Crossref (Lens and Scilit miss 0.1% and 0.2% of the records, respectively), academic search engines present lower coverage (Google Scholar does not find: 9.8%, Semantic Scholar: 10%, and Microsoft Academic: 12%). Coverage differences are mainly attributed to external factors, such as web accessibility and robot exclusion policies (39.2%-46%), and internal requirements that exclude secondary content (6.5%-11.6%). In the case of Dimensions, the only classic bibliographic database with the lowest coverage (7.6%), internal selection criteria such as the indexation of full books instead of book chapters (65%) and the exclusion of secondary content (15%) are the main motives of missing publications.
  4. Wang, S.; Ma, Y.; Mao, J.; Bai, Y.; Liang, Z.; Li, G.: Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities : On the rise of scrape-and-report scholarship in online reviews research (2023) 0.01 
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    Abstract
    Compared to previous studies that generally detect scientific breakthroughs based on citation patterns, this article proposes a knowledge entity-based disruption indicator by quantifying the change of knowledge directly created and inspired by scientific breakthroughs to their evolutionary trajectories. Two groups of analytic units, including MeSH terms and their co-occurrences, are employed independently by the indicator to measure the change of knowledge. The effectiveness of the proposed indicators was evaluated against the four datasets of scientific breakthroughs derived from four recognition trials. In terms of identifying scientific breakthroughs, the proposed disruption indicator based on MeSH co-occurrences outperforms that based on MeSH terms and three earlier citation-based disruption indicators. It is also shown that in our indicator, measuring the change of knowledge inspired by the focal paper in its evolutionary trajectory is a larger contributor than measuring the change created by the focal paper. Our study not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research.
    Date
    22. 1.2023 18:37:33
  5. Manley, S.: Letters to the editor and the race for publication metrics (2022) 0.00 
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    Date
    6. 4.2022 19:22:26
  6. Lorentzen, D.G.: Bridging polarised Twitter discussions : the interactions of the users in the middle (2021) 0.00 
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    Date
    20. 1.2015 18:30:22
  7. Milard, B.; Pitarch, Y.: Egocentric cocitation networks and scientific papers destinies (2023) 0.00 
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    Date
    21. 3.2023 19:22:14
  8. Thelwall, M.; Thelwall, S.: ¬A thematic analysis of highly retweeted early COVID-19 tweets : consensus, information, dissent and lockdown life (2020) 0.00 
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    Date
    20. 1.2015 18:30:22
  9. Cerda-Cosme, R.; Méndez, E.: Analysis of shared research data in Spanish scientific papers about COVID-19 : a first approach (2023) 0.00 
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    Date
    21. 3.2023 19:22:02
  10. Asubiaro, T.V.; Onaolapo, S.: ¬A comparative study of the coverage of African journals in Web of Science, Scopus, and CrossRef (2023) 0.00 
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    Date
    22. 6.2023 14:09:06
  11. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.00 
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    Date
    22. 6.2023 18:07:12
  12. Thelwall, M.; Kousha, K.; Abdoli, M.; Stuart, E.; Makita, M.; Wilson, P.; Levitt, J.: Why are coauthored academic articles more cited : higher quality or larger audience? (2023) 0.00 
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    Date
    22. 6.2023 18:11:50
  13. Vakkari, P.; Järvelin, K.; Chang, Y.-W.: ¬The association of disciplinary background with the evolution of topics and methods in Library and Information Science research 1995-2015 (2023) 0.00 
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    Date
    22. 6.2023 18:15:06

Authors